With more purchases being made online and the recent parade of big box retail store closings, it has marketers and consumers alike wondering if brick and mortar retail is dead or at the very least, how it can compete. For the foreseeable future, there will be many things consumers need to buy in a brick and mortar store like furniture, tools, appliances and some specialty clothing like work boots. No, retail is not dead, but many retailers are doing business both online and offline in order to compete.
While lead generation on the B2B side and e-commerce provide a real-time, tangible look into successful engagement with a direct line to conversion or sales online, for brick and mortar retail the metric has proven more elusive. How do online efforts impact offline store traffic? And, more importantly, how can data prove it?
With a media landscape that continues to evolve and a marketplace that is fierce, marketers must find every advantage to leverage digital engagement to drive both online and offline traffic to an e-commerce site or store. It is very easy to execute a digital display or paid search campaign but knowing with certainty that the media mix investments are working for offline revenue, a strategic, well thought out approach of measurement and attribution is crucial in order to prove the value of the media investment.
If we go back in time a few years, you could measure this but it required painstaking manual testing with media channels being turned on and off in selected geographies to measure the impact of advertising on offline revenue at locations. This method was extremely challenging.
Today, online to offline measurement and attribution can be achieved with advanced machine learning sophistication. It is a seamless process to measure store visits to your entire footprint of brick and mortar locations and tie that data back to touchpoints of paid search, audio, video and display. It is also possible to say with certainty of statistical significance which creative messaging helped drive incremental lift in foot traffic to stores. Data helps define the media mix, the messaging, and takes the guesswork out of budget allocations for each channel. As you gather data based on store visits, that can continuously help drive conversions and refine your A/B/n tests of messaging.
How it works
Mobile technology allows the measurement of a mobile device ID being present at a given address defined by latitude, longitude, and radius. For the purpose of cross device targeting, the mobile device ID are aligned to relevant cookies across desktop and laptops using data signals to ensure the multiple devices belong to the same person. Next, paid media can be activated across channels such as paid search, audio, video and display. The technology allows reporting back on foot traffic or store visits for users who are exposed to one of the paid ads and at a later time visited a specific location. If conducting A/B testing on messaging and audiences, artificial intelligence (AI) machine learning algorithms further help optimize towards the best set of variables that will result in a lift in foot traffic.
This is also quite effective as a competitive advantage, using available competitor retailer location data and targeting device IDs of those who have gone into that store – serving competitive messages in a timely manner during their visit or after they leave.
Using an example to illustrate the process, let’s say the company is a leading furniture retailer. If a consumer visits a competitive brand furniture store, working with a variety of data management platforms, you can target the consumer and serve up an appropriate ad from your brand to incentivise a visit or purchase – perhaps highlighting a promotion. The goal is to provide something of value that engages them in that exploratory window of time, creates great relevance for your brand and moves them toward your brand or store. The technology allows the ability to measure the success of that effort based on their device ID appearing in one of your stores.
This type of attribution requires an investment, time, and expertise but shifting to an attribution model will prove successful.
To get started, there are five core steps:
- Assess current data – this exercise will tell you if your current data is useful, the gaps in your data and the easiest path to developing an appropriate attribution model
- Define an attribution model that best fits your goals – define infrastructure and technical requirements, and systems that could impact a cohesive program
- Conduct internal meetings to bring others along in the process – helping qualify budget, KPI goals and anticipated ROI
- Develop the creative campaign based on audience insights – rather than a promotional idea in order to align with a deeper area of need for your audience
- Execute A/B/n testing – optimizing campaign concepts allows opportunity to gain the best results
Contact us to explore online to offline attribution solutions for your brick and mortar locations.